Computational Intelligence and Robotics

Research Area leader:

Overview

The CIR Research Area (RA) is one of the long term successful and focused research areas within the Faculty of Business, Justice and Behavioural Sciences. The researchers working in this research area are key members of the Computational Intelligence Research Group within the School of Computing and Mathematics, and their research is well aligned with the major themes of CRiCS (Centre for Research in Complex Systems).

The aim of this RA is to address real world problems in relation to smart information technologies in industries and Australian public sectors with the mission to serve communities through advancing frontier technology. This RA is well aligned with the national research priority Frontier Technologies for Building and Transforming Australian Industries. Under this RA, the group members are to devote their efforts to the four major themes with a wide spectrum of research concerning smart information use covering:

Intelligent Computation (IC)

Image, Video and Vision Research (IVVR)

Robotics and Automatics (RA)

Data Analytics and Making Sense of Data (DAMSD)

These research themes develop a set of innovative methodologies and techniques for smart information processing and system building for a broad range of practical applications for industries, including mining, engineering, management and health, and public sectors.

The research group accommodates a range of members from earlier career researchers, established researchers and some internationally recognised researchers. Several members have been actively engaged in conducting CRC Projects, ARC Discovery and Linkage Projects, Industry Funded Projects, and Projects funded by international funding bodies such as National Science Foundation of China (NSFC). Most of their research works have been widely published in internationally top ranked journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks, Neural Computation, Machine Learning, IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, IEEE on Multimedia and major international conference proceedings such as ICIP, ICASSP, ICME, CEC, GECCO and IJCNN.

The group enjoys several dedicated lab facilities established by CRiCS. The labs include a 3G Visualization Lab, a Computer Vision Lab, a Newcrest Mining lab, Robotics lab and High Performance Computing Lab. These labs not only enable the group members to conduct their research activities but also help PhD students to do their research.

Members

Name

Title/Position

Employer if not Faculty of Business, Justice and Behavioural Sciences, CSU

Location

Terry Bossomaier

Professor

Bathurst

Junbin Gao

Adjunct Professor

Bathurst

Michael Antolovich

Senior Lecturer

Bathurst

Xiaodi Huang

Senior Lecturer

Albury

David Tien

Senior Lecturer

Bathurst

Maumita Bhattacharya

Lecturer

Albury

Michael Kemp

Lecturer

Orange

Zahidul Islam

Lecturer

Bathurst

Manoranjan Paul

Lecturer

Bathurst

Jim Tulip

Lecturer

Bathurst

Lihong Zheng

Lecturer

Wagga Wagga

Chandana Withana

Adjunct Associate Professor

Study Group, Sydney

Sydney

Sudath Heiyantuduwage

Adjunct Lecturer

Study Group, Sydney

Sydney

Daming Shi

Adjunct Professor

Harbin Institute of Technology and Middlesex University

London, UK Harbin, China

Xia Hong

Adjunct Reader

University of Reading

Reading, UK

Paul Kwan

Adjunct Senior Lecturer

University of New England

Armidale

Zhouchen Lin

Adjunct Professor

Peking University

Beijing, China

Projects

Future Projects

Project Name

Brief Description

Investigators

Privacy of Social Network Site (SNS) users: A Data Mining Solution

The aim of the study is to explore the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of Social Network Sites (SNS) users AND provide a technical solution to the problem.

Dr Islam and Dr Yeslam Al-Saggaf

Learning on Manifolds

The long term project aims at investigating learning algorithms on manifolds in computer vision.

Prof Junbin Gao

Human Gesture and Behaviours Recognition

Human uses gestures to depict sign language to deaf people, convey messages in noisy environments, and interface with computer games. The emphasis of the project is on automatic learning of vocabularies of gestures performed by a single user or several different users under simple or complicated environment. The outcomes of this project can be applied in many interaction and computer vision applications, including computer interaction, video surveillance, rehabilitation and health care.

Dr Lihong Zheng

Current Projects

Project Name

Brief Description

Funding Body

Investigators

Online Learning for Large Scale Structured Data in Complex Situations

This project is to develop Online Learning algorithms to unlock the potential of such overwhelming data, which can lift existing applications to a new level. Reducing 1% of the total fraud loss of $3.5 trillion by research, is 35 billion dollars return. The advances in fraud detection also make Australia a safer nation which attracts more overseas investments. Advancing social networks by research will secure Australia's future share of the social networks market that has massive potential.

The Twin Measures Framework is a novel platform for analysing existing dimensionality reduction methods and the invention of new ones. This research will radically improve image analysis, with beneficial applications from pharmaceutical drug design through to border protection.

This project extends the current research on draw point boulder detection by the Mining Research Laboratory at CSU.

MMT3 Consortium

Prof Junbin Gao, Dr. Michael Antolovich and Mr. Allen Benter

Automatic and Natural Clustering of Records

In this study we aim to further improve our clustering techniques in order to group records in more meaningful clusters with automatic cluster number selection, attribute weights for clustering and so on. Clustering results will also be validated by various existing evaluation techniques and novel evaluation techniques.

Faculty of Business, Justice and Behavioural Sciences Compact Fund

Dr Islam, Prof Bossomaier, Prof Estivill-Castro, A/Prof Brankovic

Exploiting the Structural Consistency of Multi-modal Data.

The project will study the problem of multi-modal learning, and exploit the inherent complementary visual properties of multi-modal data.

Faculty of Business, Justice and Behavioural Sciences Compact Fund

Dr Xiaodi Huang and Dr Lin Wu

Biomedical Image Analysis

The research project is to develop an automatic, rotation and scale invariant segmentation method to detect contour of horse larynx from endoscopic static image or video for diagnostic as well as surgery procedure under different lighting conditions considering speed and accuracy.

CSU International PhD Scholarship

Dr Lihong Zheng and Prof Junbin Gao

Efficient Low-Resolution Image Segmentation

The similarity measure criteria play critical role in accurate segmentation. This project aims at finding an appropriate similarity measure to extract objects in an image efficiently.

Faculty of Business, Justice and Behavioural Sciences Compact Fund

Dr Lihong Zheng

Gesture recognition

This research project is to recognition human gesture based on depth images. Firstly, the discriminatory informative representation for a gesture in 3D space containing disparity gesture information will be identified. Then a 2D-MTM (motion trail model) and a three dimensional motion trail model (3D-MTM) will be built up and applied to demonstrate the accuracy and effectiveness of the proposed 3D-MTM for human gesture recognition.

CSU International PhD Scholarship

Dr Lihong Zheng

Past Projects

Project Name

Brief Description

Funding Body

Investigators

Imaging Techniques to Determine Muck Pile Ore Fragment Size In-Situ

We investigate real-time techniques for detection of larger size ore fragment in muck pile under the production environment.

This research will address issues with the constraint of surgical openings where surgeons cannot see beyond the exposed surfaces and these limitations are accentuated by the even greater restrictions of minimally invasive surgery. Limited visibility through "keyholes" during endoscopic procedures and through small incisions with ever-diminishing sizes increases the need for intra-operative image guidance. The project aims to develop a system of augmented reality enhanced image guided surgical surgery, in which the images from cameras are aligned with patient's physical position at the time of surgery. Such a "see-through" capability makes surgeries safer and more accurate. two objectives will be achieved: (1) Theoretical research on image registration based on wavelet networks. (2) Development of augmented reality enhanced image-guided surgery with see-through capability.

Image Texture Segmentation Based on Wigner Distribution in a Fractional Fourier Domain

To apply the proposed WD-FrFT to image texture segmentation. Since coarseness and directionality are two essential perceptual cues used by the human visual system for discriminating different textures, we will adopt two corresponding types of features, spatial - frequency and orientation, which will be extracted by L1 difference of Gaussian (DOG) filters and L2 wedge filters, respectively.

The similarity measure criteria play a critical role in accurate segmentation. This project aims at finding an appropriate similarity measure to extract objects in an image efficiently.

Faculty of Business, Justice and Behavioural Sciences Compact Fund

Dr Lihong Zheng and Prof Junbin Gao

Computational Intelligence for Anomaly Detection in Networks: An Investigation

This project investigates the use of computational intelligence (CI) for anomaly detection in computer networks. Also, comparative performances of existing signature-based and misuse detection approaches are investigated.

Faculty of Business, Justice and Behavioural Sciences Compact Fund

Maumita Bhattacharya and Dr Tanveer Zia

Age Care Workforce Reform - Building Communities of Practice Around the Prevention of Functional Decline in the Community

This research seeks to investigate whether improved training and use of technology by clinicians (support workers) and training of volunteers improves human resource management outcomes among employees and volunteer carers who are involved in reducing the rate of functional decline among seniors. This research involves the use of experiments and pre-post surveys of subjects. Various data mining techniques are applied on the survey data for extracting the patterns and information in order to evaluate the impact of various interventions.

The main aim of this project is to suggest reasons and possible remedies of the functional decline for the employees and care receivers of Hobart District Nursing. We apply data mining and other data analyses on the collected data.

This project is to develop novel algorithms that are able to handle dual-relational data over a combined graph for co-ranking images and tags simultaneously. In particular, a co-ranking scheme for images and their associated tags in a heterogeneous graph will be designed and tested on real datasets, together with a prototype system.

Faculty of Business, Justice and Behavioural Sciences Compact Fund

Dr Xiaodi Huang and Dr Lin Wu

Data Cleansing and Data Pre-processing Techniques

In this study we develop novel data cleansing techniques for improving data quality. The improved data will then be used in making better decision for an organisation.

We have also developed an Agent Based Modeling (ABM) which is powered by data mining techniques in order to simulate the property market and thereby predict and assess property prices. We are also developing various techniques for more acceptable property valuation

Faculty of Business, Justice and Behavioural Sciences;Compact Fund

Dr Islam, Prof Terrry Bossomaier, Prof Junbin Gao

Data Mining threats on Privacy of Social Network Site (SNS) users

The aim of the study is to explore the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of Social Network Sites (SNS) users.

Faculty of Business, Justice and Behavioural Sciences Compact Fund

Dr Yeslam Al-Saggaf and Dr Islam

Learning based Boundary/Contour Extraction

Automatic Number Plate Recognition (ANPR) is required due to increasing traffic management. This project will deliver a technology capable of efficiently extracting trustful boundary feature for car number plates.

CSU Small Grant

Dr Lihong Zheng

Efficient Character Segmentation in ANPR

This project will deliver an accurate and efficient technology to cut characters from located images of car number plates.

Faculty Seed Grant

Dr Lihong Zheng

A Novel Decision Tree Classification Algorithm

The aim of this project is to develop a novel decision tree algorithm that will extract useful patterns (that are currently ignored by existing algorithms) from a data set. We study various existing classification algorithms such as Decision tree algorithms, Neural networks, Bayesian algorithms and Genetic algorithms. We propose some modifications to existing algorithms and test the algorithm by applying it on a number of data sets. We compare the efficiencies of the proposed algorithm with various existing algorithms such as See 5, J48, and REPTree. The efficiencies are evaluated based on quality of extracted pattern, simplicity of the trees, Performance and Significance of logic rules. Our initial experimental results are very encouraging.